stock_rating

Predicting Daily Stock Ratings

Streamlined daily data points of financial statement, market, forecast, and analyst rating history for over 3,000 publically-traded companies. Using an artificial neural network, predicted a stock's mean daily analyst rating. Utilized Google Cloud Platform, to train and test multiple architectures. On a scale from 1 (Strong Buy) to 5 (Strong Sell), final model performed with a MAE of 0.73. Designed a Streamlit web app and deployed it via Heroku, enabling users to visualize stock performance and obtain that day's stock rating.

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Links

Medium - Blog Post

Project Presentation

Streamlit Demo

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Motivation

My motivation for this data science project was to predict a stock’s analyst recommendation for a given stock, on a given day. Essentially, my goal was to create an algorithm that does the manual work analysts do — but with deep learning. While no algorithm can substitute the need for professional judgment in the investment decision-making process, this model can be used as a quick sanity check or temperature gauge.

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Models

1. Linear Regression w/ Lasso Regularization
2. Sequential Neural Network

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Data Used

Zack's Analyst Ratings: This dataset contained two years of daily historical analyst ratings (from over 185 brokerage firms) for over 3,000 publicly traded companies.

Shardar Equity Prices: Updated daily, this database provides End-Of-Day (EOD) price data

Core US Fundamentals Data: Updated daily, this database provides up to 20 years of history, for 150 essential fundamental indicators (financial statement line items) and financial ratios, for more than 14,000 US public companies

Financial Modeling Prep: Updated daily, this API provided daily discounted cash flow, and earnings surprise data

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Process

1. Fetch data from Quandl sources, calculate autoregressive features and financial ratios - historical_data_quandl.ipynb
2. Fetch data from FinancialModelingPrep - historical_data_financial_modeling.ipynb
3. Merge DataFrames and prep for training - merge_prep.ipynb
4. Train/Test Linear Regression Model - modeling_linear_regression.ipynb
5. Train/Test Neural Network Model - modeling_ann_final.ipynb
6. Create Streamlit App - streamlit_app.py

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Tools and Packages

1. Python
2. Pandas
3. NumPy
4. matplotlip
5. scikit-learn
6. Google Cloud Platform
7. TensorFlow
8. Keras
9. Bokeh
10. Plotly
11. Streamlit
12. Heroku